Models › EXAONE-3.5 family

EXAONE-3.5-7.8B-Instruct

LGAI-EXAONE exaone out 23 months ago

7.82B parameters, text in, text out, 32K tokens of context.

Verified 2026-10-25 Rank #471 in LLMs · score 77.3 method v1 Sources 3 Downloads 30d 247.5k · #235 of 619 tracked

Can I run it?4.7GB at INT4Estimatefits a 16 GB cardmethod
Can I use it?exaoneCustom license; read the repo LICENSE before commercial hostinglicence file2026-10-24
Do people use it?247.5kdownloads, 30 days#235 of 619 we track · top 38%+0 likes, downloads −1% this week159 likesHugging Face2026-10-24

Worth reading

Picked by rule from 1 tracked piece: the lab's own post, the most shared, a field report, the latest.

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

No API provider serves it, so its downloads are people running it themselves: 27 quantized builds and 10 Spaces.

  • As a service0no API provider listed
  • On own hardware27quantized builds
  • Built on292136 fine-tunes · 156 adapters · 0 merges
  • In apps10Hugging Face Spaces

Top Spaces running it: mayafree/Model-Genome-Korea ♥ 33

Will it fit?

Weights only, an estimate: leave room for the KV cache. Method

Fits a 16 GB card at INT4

About 4.7 GB at INT4, 9.4 GB at FP8, 19 GB at BF16: weights only, so leave room for the KV cache.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 4.7 GB of weights (estimate; KV cache and activations extra)INT44.7 GBFP8: about 9.4 GB of weights (estimate; KV cache and activations extra)FP89.4 GBBF16: about 19 GB of weights (estimate; KV cache and activations extra)BF1619 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 4.7 GB of weights (estimate; KV cache and activations extra)INT44.7 GBFP8: about 9.4 GB of weights (estimate; KV cache and activations extra)FP89.4 GBBF16: about 19 GB of weights (estimate; KV cache and activations extra)BF1619 GB

Estimate params × bytes per param × 1.2 · how we estimate

The story so far

Weights, every dated article, and repo commits on one rail.

1 dated article and 3 repo commits so far

Teal is the weights (2024-12-01), amber triangles are 1 dated article (each links to it), grey ticks are repo commits.

JanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2026-02-06: 1 repo commit2024-12-11: 1 repo commit2024-12-09: 1 repo commitOpen-sourcing Three EXAONE 3.5 Models: 2.4B, 7.8B, 32B · 2024-12-09Weights 2024-12-01 · 2024-12-01Weights 2024-12-01now
JanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2026-02-06: 1 repo commit2024-12-11: 1 repo commit2024-12-09: 1 repo commitOpen-sourcing Three EXAONE 3.5 Models: 2.4B, 7.8B, 32B · 2024-12-09Weights 2024-12-01 · 2024-12-01Weights 2024-12-01now

What people built on it

319 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

319 derivatives on the Hub, led by 156 adapters

From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.

27 quantized (Hugging Face model tree)136 fine-tunes (Hugging Face model tree)156 adapters (Hugging Face model tree)27 quantized136 fine-tunes156 adapters
27 quantized (Hugging Face model tree)136 fine-tunes (Hugging Face model tree)156 adapters (Hugging Face model tree)27 quantized136 fine-tunes156 adapters

All variants →

Reference

Everything else, folded: the full tables, every source.

Benchmarks

7 rows

Benchmarks 7 rows

measured third-party leaderboard rowauthor-reported number from the model card or eval files (7)No confidence intervals unless the source gave them; settings differ between rows, so compare within a benchmark, not across. Date = the benchmark's own date when it gives one, else when we read it.
BenchmarkValueSetting / notesReported byDate
author-reported AlpacaEval · Chat and follow instructions54.2EXAONE 3.5 7.8Bmodel author2026-10-07
author-reported Arena-Hard · Chat and follow instructions68.7EXAONE 3.5 7.8Bmodel author2026-10-07
author-reported IFEval · Chat and follow instructions78.9EXAONE 3.5 7.8Bmodel author2026-10-07
author-reported LiveBench · Reason through hard problems39.8EXAONE 3.5 7.8Bmodel author2026-10-07
author-reported LogicKor9.08EXAONE 3.5 7.8Bmodel author2026-10-07
author-reported MT-Bench · Chat and follow instructions8.29EXAONE 3.5 7.8Bmodel author2026-10-07
author-reported MT-Bench · Chat and follow instructions7.96EXAONE 3.5 7.8Bmodel author2026-10-07

What the card says

2 cited sentences

Summary generated, cited

EXAONE-3.5-7.8B-Instruct is a 7.82B parameter bilingual instruction-tuned generative model developed by LG AI Research.[1][2] The model supports English and Korean languages and provides a context length of up to 32,768 tokens.[3][4]

  1. parameters human
  2. “developed and released by LG AI Research”
  3. languages
  4. “Context Length: 32,768 tokens”

Generated 2026-10-07 15:10 UTC by parasail-gemma-4-26b-a4b-it; each numeral points at the fact it rests on. Not a verdict.

Use-case evidence

3 use cases

What it is good at 3 use cases

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

22 fields

Listed in LLMs

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesExaoneForCausalLM
Model typeexaone
Dtypefloat32
Params by dtypeF32: 7,818,448,896
Size bandM
MoEno
Context length32,768 (config.json max_position_embeddings)
Languagesen, ko
PapersarXiv:2412.04862
Licenseexaone (other)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · ExaoneForCausalLM · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · ExaoneForCausalLM · 773cb938fe9484a8f7e7e46fb1b1fe96412c59ed

vLLM registry (7050ef881ff0835f6890a47db1859a24c65481d6) · SGLang registry (773cb938fe9484a8f7e7e46fb1b1fe96412c59ed) · Matched by architecture class name against each project's registry on main; 'supported' means the architecture is registered, not that this checkpoint was tested.

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for EXAONE-3.5-7.8B-Instruct: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)19 GB · Single GPU (<24 GB)
FP8 (1 byte/param)9.4 GB · Single GPU (<24 GB)
INT4 (0.5 bytes/param)4.7 GB · Single GPU (<24 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra.

All variants

40 variants

Lineage

Original model Family: LGAI-EXAONE/EXAONE-3.5 319 derivatives on the Hub

finetune: 136quantized: 27adapter: 156

Top variants (40 shown, ranked by downloads)

Relation
Quant
Size
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
LGAI-EXAONE/EXAONE-Deep-7.8B finetune — 7.82B M other LGAI-EXAONE 103 1.3M
lmstudio-community/EXAONE-3.5-7.8B-Instruct-GGUF quantized GGUF — — other lmstudio-community 2 88.7k
Mungert/EXAONE-Deep-7.8B-GGUF finetune GGUF — — other Mungert 5 29.4k
bartowski/EXAONE-3.5-7.8B-Instruct-GGUF quantized GGUF — — other bartowski 16 27.3k
mradermacher/EXAONE-3.5-7.8B-Instruct-i1-GGUF quantized GGUF — — other mradermacher 2 13.9k
mobilint/EXAONE-3.5-7.8B-Instruct quantized — 419M S other mobilint 0 12.8k
OpenLLM-Korea/EXAONE-Deep-7.8B finetune — 7.82B M other OpenLLM-Korea 0 4.9k
QuantFactory/EXAONE-Deep-7.8B-GGUF finetune GGUF — — other QuantFactory 3 4.2k
mradermacher/EXAONE-3.5-7.8B-Instruct-GGUF quantized GGUF — — other mradermacher 1 3.8k
second-state/EXAONE-3.5-7.8B-Instruct-GGUF quantized GGUF — — other second-state 0 3.4k
MaziyarPanahi/EXAONE-3.5-7.8B-Instruct-GGUF quantized GGUF — — — MaziyarPanahi 1 3k
hymmmm/exaone-3.5-7.8b-emt-awq quantized awq 7.82B M other hymmmm 0 3k
tensorblock/EXAONE-3.5-7.8B-Instruct-GGUF quantized GGUF — — other tensorblock 0 2.1k
gaianet/EXAONE-3.5-7.8B-Instruct-GGUF quantized GGUF — — other gaianet 0 1.7k
ChanLumerico/EXAONE-3.5-7.8B-Instruct-Yaho finetune GGUF 7.82B M other ChanLumerico 0 1.4k
Jongbin-kr/exaone_7b_lora_rtlcoder_ratio0.2 adapter — — — — Jongbin-kr 0 1.2k
huggingface-KREW/EXAGIRL-7.8B-Instruct finetune — 7.82B M other huggingface-KREW 6 686
mlx-community/EXAONE-3.5-7.8B-Instruct-3bit quantized — 7.82B M other mlx-community 0 959
mobilint/EXAONE-3.5-7.8B-Instruct-Batch16 quantized — 419M S other mobilint 0 901
Jongbin-kr/llama3_convfin_lofit_top32_3ep finetune — 7.82B M — Jongbin-kr 0 899
DevQuasar-6/LGAI-EXAONE.EXAONE-3.5-7.8B-Instruct-GGUF quantized GGUF — — — DevQuasar-6 0 886
huggingface-KREW/EXAONE-3.5-7.8B-Instruct-KoCulture finetune — 7.82B M other huggingface-KREW 6 493
Jongbin-kr/exaone-7b-verireason-reproduced-1513-fullft-epoch4 finetune — 7.82B M other Jongbin-kr 0 724
hymmmm/exaone-3.5-7.8b-emt-qlora-awq-v2 quantized awq 7.82B M other hymmmm 0 722
alexander583/EXAONE-3.5-7.8B-Instruct-Q4-mlx quantized mlx 7.82B M other alexander583 0 670
Jongbin-kr/exaone-3.5-7.8b-verireason-full-ft-reasoning-co-ratio1.0-epoch3 finetune — 7.82B M — Jongbin-kr 0 582
Jongbin-kr/exaone3.5_lofit_convfinqa_top32 finetune — 7.82B M — Jongbin-kr 0 561
FloatDo/EXAONE-3.5-7.8B-Instruct-GGUF quantized GGUF — — other FloatDo 0 520
Jongbin-kr/exaone_7b_lora_rtlcoder_ratio1.0 adapter — — — — Jongbin-kr 0 465
furiosa-ai/EXAONE-3.5-7.8B-Instruct finetune — — — other furiosa-ai 0 425
werty1248/EXAONE-3.5-7.8B-Stratos-Ko finetune — 7.82B M — werty1248 1 323
itlwas/EXAONE-3.5-7.8B-Instruct-Q4_K_M-GGUF quantized GGUF — — other itlwas 0 325
NikolayKozloff/EXAONE-3.5-7.8B-Instruct-Q8_0-GGUF quantized Q8_0 — — other NikolayKozloff 1 264
hoySky92/EXAONE-U-CURATE-Random1K-1epoch-v1 finetune — 7.82B M other hoySky92 0 301
hymmmm/exaone-3.5-7.8b-emt-chatbot finetune — 266K S other hymmmm 0 299
Open4bits/EXAONE-3.5-7.8B-Instruct-GGUF quantized GGUF — — other Open4bits 0 270
Damisoda/EXAONE-Dementia-SAR adapter — — — other Damisoda 5 18
xxccho/EXAONE-3.5-7.8B-Instruct_lg_convfin_mcq_pc_lora_r64_DR1.0 adapter — — — — xxccho 0 248
xxccho/EXAONE-3.5-7.8B-Instruct_lg_convfin_mcq_reasoning_pc_lora_r64_DR1.0 adapter — — — — xxccho 0 239
alexgusevski/EXAONE-3.5-7.8B-Instruct-q3-mlx quantized mlx 7.82B M other alexgusevski 0 234

Repository changelog

3 commits

Repository changelog 3 commits

DateCommitSHA
2026-02-06 Update README.md and config.json for Transformers v5 553ea25
2024-12-11 Update README.md 0ff6b5e
2024-12-09 Initial commit 496aef0

Articles and coverage

1 piece

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.

Official1

lgresearch.ai2024-12-09officialabout the EXAONE 3.5 familyHN 13 pts · 4 comments1,244 words
LG AI Research released three EXAONE 3.5 models including a 2.4B, 7.8B, and 32B variant. All three models handle contexts of up to 32K tokens and ranked first in average scores for Instruction Following capabilities.generated, cited

Papers

1 paper

Papers 1

arXiv links found in the model card and its tags. "Peer-reviewed" is shown only with evidence: a published venue in OpenAlex (measured) or the authors' own journal reference on arXiv (reported).

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3 models

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  • Llama-3.1-8B-Instruct · 8.03B
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  • Qwen3-8B · 8.19B
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Sources

3 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:39 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:46 UTC · Summary: 2026-10-07 15:10 UTC · Last verified 2026-10-25

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